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Abstract With the increasingly severe energy supply and environmental pressures, high-end equipment is gradually adopted to reduce the carbon emissions of manufacturing industry which makes its low-carbon structural design a critical research hotspot. The best structural scheme can be got by multi-attribute decision-making (MADM) with design requirements. However, the decision-making attributes in the structural design of high-end equipment are too many at first and low-carbon attributes are seldom fully considered. Moreover, there are a large amount of related data with linguistic vagueness, interval uncertainty, and information incompleteness, which fail to be handled simultaneously. There, this paper proposes an integrated MADM method of low-carbon structural design for high-end equipment based on attribute reduction considering incomplete interval uncertainties. First, distribution reduction of low-carbon structural design is carried out to obtain the minimum attribute set and encompass low-carbon attributes comprehensively. Second, a collaborative filtering algorithm is utilized to complete the missing data in the subsequent design process. Third, interval rough numbers (IRNs) are integrated into DEMATEL-ANP (DANP) and multi-attribute border approximation area comparison (MABAC) to quickly rank the alternative schemes for high-end equipment and determine which is the best. The rationality and robustness of the proposed method are verified through the case study and comparative analysis of a hydraulic forming machine.

Abstract Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis.

Abstract Blank holder force (BHF) is a crucial parameter in deep drawing, having close relation with the forming quality of sheet metal. However, there are different BHFs maintaining the best forming effect in different stages of deep drawing. The variable blank holder force (VBHF) varying with the drawing stage can overcome this problem at an extent. The optimization of VBHF is to determine the optimal BHF in every deep drawing stage. In this paper, a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently. An improved “Quasi-oppositional” strategy is added to Jaya algorithm for improving population diversity. Meanwhile, an innovated stop criterion is added for better convergence. Firstly, the quality evaluation criteria for wrinkling and tearing are built. Secondly, the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling. Finally, the optimization models are established and solved by the improved QO-Jaya algorithm. A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm. The optimization results are compared with that obtained by other algorithms based on the TOPSIS method.

Abstract With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efficient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difficult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identification is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identification models. The experimental results proved the superiority of the HDT framework for human locomotion mode identification.

The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching–learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.

Abstract Network function virtualization (NFV) is an appealing solution that transforms complex network functions from dedicated hardware to software instances running in a virtualized environment. However, some new challenges will arise when deploying virtual network functions to meet the needs of NFV and edge‐computing (EC) enabled 5G networks. In this paper, we focus on the service function chain (SFC) orchestration problem for EC‐enabled networks to maximize the profit of network service providers. First, the mathematical model of SFC orchestration in NFV and EC‐enabled networks is defined. Then, a two‐stage heuristic algorithm is proposed to optimize the total revenue. Finally, the performance of the method is evaluated by simulation experiments and the results show its effectiveness.

Soft grippers based on stimuli‐responsive materials show great promise to perform safe interaction and adaptive functions in unstructured environments. Hence, improving flexibility and designability of the stimuli‐responsive soft grippers for better grasping ability are highly desired. Inspired by the biological structure of octopuses, a class of temperature‐driven polylactic acid grippers is fabricated by 4D printing in this work, which consists of two types of bilayer structures, separately imitating the web and tentacles of an octopus. Compared with the traditional pure‐bending soft grippers, the integrated structure results in a 1.5 times wider reachable area and enhanced flexibility. The complex grasping behaviors are predicted with the 4D printing parameters (i.e., printing paths and printing speeds) using the reduced bilayer plate theory. Moreover, a method for determining the printing parameters of the gripper is provided to realize the desired grasping behavior depending on different objects. The feasibility of the design method is experimentally verified by gasping three distinctive objects, including an egg, a weight, and a Metatron's cube, which is in good agreement with the simulation results. Herein, a promising strategy is provided to achieve the easy‐fabricated, low‐cost, and versatile soft graspers using smart materials.

The integration of green design into building construction is a necessary process in today’s world to address environmental issues and achieve sustainable development. However, when evaluating green building design schemes, various factors are intertwined with a high degree of complexity and uncertainty. To realise rational decision-making about green building design schemes, this paper first adopts the mixed techniques of triangular fuzzy numbers, quality function deployment, and Best–Worst Method. It aims to analyse the complex factor relationship between customer needs and green building design technical features and to solve the optimal green building design index weight allocation. Next, a hybrid fuzzy multi-criteria decision-making (MCDM) method integrating triangular fuzzy numbers, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, and Grey Correlation (GC) are proposed to evaluate green building design scheme performance. Finally, an example of a green design project for a hotel building is selected for application validation and analysis in comparison with the existing Complex Proportional Assessment, VlseKriterijuska Optimizacija I Komoromisno Resenje, and DEMATEL-ANP methods. These analyses demonstrate the stability and validity of the results, as well as the rationality and practicability of the proposed triangular fuzzy QFD–MCDM method. This research is a guide to the problem of evaluating green building design schemes.

Abstract Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

With the rapid expansion of Industrial Internet of Things, cloud computing and artificial intelligence, many intelligent information services have been developed in smart factories. One of the most important applications is helping factory managers predict the quality of assembled products. Traditional prediction methods of assembly quality mainly focus on building classification or regression models with high accuracy. However, less attention is paid to high-dimensional and imbalanced data, which is a special but common scenario at real-life assembly quality prediction. In this paper, we first use random forest to reduce dimension and analyze critical-to-quality characteristics. Then, a SMOTE-Adaboost method with jointly optimized hyperparameters is proposed for imbalanced data classification in assembly quality prediction. In addition, edge computing is introduced to improve the efficiency and flexibility of quality prediction. Finally, the practicality and effectiveness of the proposed method are verified by a case study of wheel bearing assembly line, and the experimental results show that the proposed method is superior to other classification methods in assembly quality prediction.

As an important link to guarantee normal industrial production, equipment maintenance plays an increasingly key role in enhancing the competitiveness of enterprises and supporting green smart manufacturing. This paper aims to promote the implementation of predictive maintenance for complex equipment and improve the green performance of the maintenance service process. A structural framework of information sharing and service network is introduced to build a ubiquitous state data awareness environment for predictive maintenance service. Subsequently, an integrated mathematical problem model that consists of carbon emission objective and extended maintenance cost objective is constructed. Then an improved NSGA-II algorithm is utilized to solve this complicated two-objective optimization problem. In response to deal with the uncertainties of maintenance service environment and inaccuracy of prediction, a data-driven dynamic adjustment strategy is applied. A grinding roll fault case of a large vertical is used to demonstrate the effectiveness of this proposed approach.

Structural optimization of complex mechatronic components may involve heterogeneous competing performance indices, including the cost, fixation, benefit, and deviation ones. However, such optimization problems with heterogeneous objectives have not been investigated so far. In this paper, a novel interval heterogeneous-objective robust optimization approach is proposed for complex mechatronic components. First, a unified interval heterogeneous-objective robust optimization model is constructed for mechatronic components with the uncertainties described as interval variables. Subsequently, a new interval robust equilibrium optimization algorithm is proposed to solve the interval heterogeneous-objective robust optimization model. Specifically, the unified formulas for assessing the robustness of interval heterogeneous-objective performance indices and the robust equilibrium among them are derived at first. Then, the preferential guidelines considering the robust equilibrium among all the objective and constraint performance indices are proposed for the direct ranking of various design vectors, and finally, the heterogeneous-objective robust equilibrium optimization of complex mechatronic components is realized by integrating the Kriging technique and the nested genetic algorithm. The feasibility and effectiveness of the proposed heterogeneous-objective robust optimization approach are verified by a numerical example and a case study.

Sandwich structures are important innovative multifunctional structures with the advantages of low density and high performance. Creative design for sandwich structures is a design process based on sandwich core structure evolution mechanisms, material design method, and panel (including core structure and facing sheets) performance prediction model. The review outlines recent research efforts on creative design for sandwich structures with different core constructions such as corrugated core, honeycomb core, foam core, truss core, and folded cores. The topics discussed in this review article include aspects of sandwich core structure design, material design, and mechanical properties, and panel performance and damage. In addition, examples of engineering applications of sandwich structures are discussed. Further research directions and potential applications are summarized.

With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.

Nowadays, a large amount of energy is consumed by buildings in the city. Among them, industrial energy consumption accounts for a large proportion due to the high power of manufacturing devices in the job-shop. To achieve higher energy-efficiency, lots of scheduling methods in the job-shop are developed. However, most of the current scheduling method is based on the prior-experimental data and the schedule of the production cycle cannot be adjusted according to energy status of manufacturing devices. Thus a dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop. First, a novel process of “scheduling-monitoring-updatingoptimizing”is implemented in the proposed system. In the scheduling model, tool aging condition is considered along with the geometry information of the work-piece to estimate energy consumption of the manufacturing process. At last, a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule. The proposed system is implemented in a production cycle which contains 36 operations of six work-pieces and ten machine tools to prove its validity.

Conceptual design plays an important role in a product development process. Significant development of technologies in the cyber-physical system (CPS) provides innovative approaches for product conceptual design. This paper proposes an architecture of CPS for conceptual design that realizes the acquisition of real-time physiological data from the physical world and the feedback of psychological states from the cyber world. As understanding and meeting the needs of customers have been recognized as significant aspects for conceptual design, an intelligent psycho-physiological approach that incorporates electroencephalogram (EEG) into the Kano model is adopted in this CPS for real-time customer needs analysis. The sample entropy (SampEn) extracted from EEG data is an endogenous neural indicator for customers' psychological states. A support vector machine using this SampEn as input is trained for classifying different categories of quality attributes defined in the Kano model. A case study is conducted to testify the feasibility of the approach proposed in this paper.

Energy Internet represents a critical breakthrough that allows traditional energy to be transformed into intelligent energy. In this regard, the reliability apportionment technologies for modern products within Energy Internet deserve more attention because they can ensure the reliability optimization of different functional modules of systems. And because of the growingly serious energy crisis, environmental protection has become much more urgent, which requires industrial products to be more environmental friendly. However, traditional reliability apportionment methods can not do this. First, in design phase of product, most of them tend to ignore the environmental attributes, including carbon emissions and resource efficiency. Second, they fail to process the uncertainties of reliability apportionment, which is inevitable and important efficiently. Third, they pay insufficient attention to the correlations among product subsystems, which greatly influence the different product functions. To overcome these drawbacks, a reliability apportionment model based on the interval analysis and decision-making trial and evaluation laboratory is proposed. The validity of the proposed method is demonstrated by a case study.

In order to solve the problems of high gas consumption and low battery power in hybrid electric buses, the logic threshold control strategy is optimized by the key technology research of driving conditions, control strategy, and so on. First, driving condition data of hybrid electric buses are collected and analyzed, and three typical working conditions are selected. Second, the torque distribution control strategy is investigated based on the instantaneous optimization algorithm. The objective function of the instantaneous equivalent fuel consumption is derived. The process of solving the objective function is attributed to linear programming problem of the nonlinear objective function. The SIMULINK model of instantaneous torque distribution strategy is established based on equivalent fuel consumption. On this basis, the SIMULINK model of the function which can be integrated into the vehicle control model is established. Then, considering the actual driving conditions of the bus, the vehicle control strategy model is established in the MATLAB/SIMULINK environment, and the vehicle model established by the cruise software. Finally, the vehicle model is simulated under three representative working conditions. The main contribution of this paper is the optimized torque distribution control strategy to control hybrid electric bus's energy distribution and reduce emissions. The strategy can be obtained by combining the logic threshold torque distribution control strategy, along with the optimal engine torque and motor torque, which can be obtained by solving the objective function of instantaneous equivalent fuel consumption. Results show that compared with the logic threshold value torque distribution control strategy, the energy control strategy of the instantaneous optimization algorithm can further reduce gas consumption and maintain the state of charge value balance of the power battery.

Cloud manufacturing (CMfg) is a new service-oriented production paradigm from the wide application of cloud computing for the manufacturing industry. The aim of this manufacturing mode is to provide resource-sharing and on-demand manufacturing mode to improve operation efficiency. Resource allocation is considered as a crucial technology to implement CMfg. Traditional resource allocation approaches in CMfg mainly focus on the optimal resource selection process, but the energy consumption for manufacturing resources is rarely considered. In response, this paper proposes a hybrid energy-aware resource allocation approach to help requestors acquire energy-efficient and satisfied manufacturing services. The problem description on energy-aware resource allocation in CMfg is first summarized. Then a local selection strategy based on fuzzy similarity degree is put forth to obtain appropriate candidate services. A multi-objective mathematical model for energy-aware service composition is established and the nondominated sorting genetic algorithm (NSGA-II) is adopted to conduct the combinatorial optimization process. Furthermore, a technique for order preference by similarity to an ideal solution is used to determine the optimal composite services. Finally, a case study is illustrated to validate the effectiveness of the proposed approach.

Aluminum alloy thin-walled structures have been well used in applications of energy absorption. In the present work, a bioinspired design strategy for aluminum alloy thin-walled structures is proposed to improve the performance of out-of-plane crashworthiness by altering the material distribution. According to the proposed strategy, a novel fractal thin-walled triangle column (FTTC) is designed, which is composed by iteratively applying the affine transformation of a base triangle up to 2nd-order. The finite element model is established to investigate the out-of-plane crashworthiness of FTTC and validated by experiment results. The numerical analysis of the crashworthiness of FTTC with different fractal orders (0th, 1st and 2nd) are performed, and the results show that 1st- and 2nd-order FTTC enhance the energy absorption of structures and crush force efficiency. In particular, 2nd-order FTTC has better energy absorption ability due to the optimal distribution of materials, which are efficiently organized by the proposed bioinspired design strategy. In addition, a parameter study is performed to investigate the effect of FTTC geometric details on the crushing procedure. The collapse mode shows that it tends to change from unstable to stable with the increase in thickness and side length and the decrease in height. Moreover, a positive relevant relationship is identified between the thickness and the crashworthiness for FTTC.

Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process–techniques for order preference by similarity to an ideal solution and analytical hierarchy process–gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.

This article proposes a machine learning–based travel mode detection method using urban residents’ travel routes as the data source, collected via smartphone global positioning system modules. A data-driven machine learning strategy was chosen in the model construction. This study performed data cleaning and mining on over 4400 pieces of urban resident travel records containing several millions of global positioning system tracking points. Series of characteristic values of speed, travel distance, and direction are calculated, which reflect the travel mode of smartphone holders. In travel mode identification, first, the transition regions of travel segments of different travel modes are effectively distinguished; then, continuous tracking points for single-mode travel are connected into single-mode travel segments. The travel mode of the surveyed subjects is identified based on the calculated features of average speed, average acceleration, and average change of direction within each single-mode segment. The random forest method is chosen as the basis model to classify travel mode. Three-quarters of the travel records were used to construct the random forest classifier, and the detection accuracy of the established model for the remaining ¼ of the travel record reached 94.4%. The proposed method uses massive smartphone global positioning system tracking points as the basis; the detection results are consistent with manually collected prompted recall survey records.

Core panels inspired by origami have the advantages of force allocation and energy dissipation. Used as a sandwich core, the three-dimensional panels could be created using various origami patterns. The panel is composed of the element whose structure is inspired by origami. The orthopyramid-like origami element has a tip of joined-together side triangles. Through shape deformation, it could exhibit potential mechanical performances. Owing to its deformation when collision occurs, the structure could be employed for load damping conditions. This study focuses on nine different orthopyramid-like core panels through changing the similarity parameter value and the number of edges. The experiment and numerical simulation of compression and impact tests are carried out to perform the parametric study on the influences of the similarity parameter and the number of edges. The results show that with the increase of these two parameters, the panel tends to be softer, greatly influencing the load damping ability. Moreover, the structure parameters are optimized by the Genetic Algorithm integrated with the finite element analysis model.

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach.

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

In order to achieve high punching precision, good operational reliability and low manufacturing cost, the structural optimization of a high-speed press in the presence of a set of available alternatives comprises a heterogeneous multiple-attribute decision-making (HMADM) problem involving deviation, fixation, cost and benefit attributes that can be described in various mathematical forms due to the existence of multi-source uncertainties. Such a HMADM problem cannot be easily resolved by existing methods. To overcome this difficulty, a new heterogeneous technique for order preference by similarity to an ideal solution (HTOPSIS) is proposed. A new approach to normalization of heterogeneous attributes is proposed by integrating the possibility degree method, relative preference relation and the attribute transformation technique. Expressions for determining positive and negative ideal solutions corresponding to heterogeneous attributes are also developed. Finally, alternative structural configurations are ranked according to their relative closeness coefficients, and the optimal structural configuration can be determined. The validity and effectiveness of the proposed HTOPSIS are demonstrated by a numerical example. The proposed HTOPSIS can also be applied to structural optimization of other complex equipment, because there is no prerequisite of independency among various attributes for its application.

In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN), interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC) machine tool.

To solve the product data consistency problem which is caused by the portable system that cannot conduct real-time update of product data in mobile environment under the mass customization production mode, a new product data optimistic replication method based on log is presented. This paper focuses on the design thinking provider, probing into a manufacturing resource design thinking cloud platform based on manufacturing resource-locating technologies, and also discuss several application scenarios of cloud locating technologies in the manufacturing environment. The actual demand of manufacturing creates a new mode which is service-oriented and has high efficiency and low consumption. Finally, they are different from the crowd-sourcing application model of Local-Motors. The sharing platform operator is responsible for a master plan for the platform, proposing a open interface standard and establishing a service operation mode.